Search Results for author: Tirza Routtenberg

Found 23 papers, 3 papers with code

GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering

no code implementations28 Nov 2023 Itay Buchnik, Guy Sagi, Nimrod Leinwand, Yuval Loya, Nir Shlezinger, Tirza Routtenberg

Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation.

NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation

1 code implementation6 Sep 2023 Mengyuan Zhao, Guy Revach, Tirza Routtenberg, Nir Shlezinger

Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots.

Super-Resolution

Non-Bayesian Post-Model-Selection Estimation as Estimation Under Model Misspecification

no code implementations22 Aug 2023 Nadav Harel, Tirza Routtenberg

We show that the proposed performance bounds are more informative than the oracle Cram$\acute{\text{e}}$r-Rao Bound (CRB), where the third interpretation (selective inference) results in the lowest mean-squared-error (MSE) among the estimators.

Model Selection

Estimation of Complex Valued Laplacian Matrices for Topology Identification in Power Systems

no code implementations7 Aug 2023 Morad Halihal, Tirza Routtenberg, H. Vincent Poor

In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems.

Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach

no code implementations31 May 2023 Lital Dabush, Tirza Routtenberg

The proposed approaches are based on the representation of network data as the output of a graph filter with a given graph topology.

Protection Against Graph-Based False Data Injection Attacks on Power Systems

no code implementations21 Apr 2023 Gal Morgenstern, Jip Kim, James Anderson, Gil Zussman, Tirza Routtenberg

We present the GFDI attack as the solution for a non-convex constrained optimization problem.

Barankin-Type Bound for Constrained Parameter Estimation

no code implementations17 Apr 2023 Eyal Nitzan, Tirza Routtenberg, Joseph Tabrikian

The constrained Barankin-type bound (CBTB) is a nonlocal mean-squared-error (MSE) lower bound for constrained parameter estimation that does not require differentiability of the likelihood function.

Direction of Arrival Estimation Vocal Bursts Type Prediction

Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals

1 code implementation16 Apr 2023 Itay Buchnik, Damiano Steger, Guy Revach, Ruud J. G. van Sloun, Tirza Routtenberg, Nir Shlezinger

In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach.

Vocal Bursts Intensity Prediction

Efficient Graph Laplacian Estimation by Proximal Newton

no code implementations13 Feb 2023 Yakov Medvedovsky, Eran Treister, Tirza Routtenberg

The Laplacian-constrained Gaussian Markov Random Field (LGMRF) is a common multivariate statistical model for learning a weighted sparse dependency graph from given data.

Graph Learning

GSP-Based MAP Estimation of Graph Signals

no code implementations23 Sep 2022 Guy Sagi, Tirza Routtenberg

In this paper we propose two new estimators that are both based on the Gauss-Newton method: 1) the elementwise graph-frequency-domain MAP (eGFD-MAP) estimator; and 2) the graph signal processing MAP (GSP-MAP) estimator.

Widely-Linear MMSE Estimation of Complex-Valued Graph Signals

no code implementations22 Aug 2022 Alon Amar, Tirza Routtenberg

For general Bayesian estimation of complex-valued vectors, it is known that the widely-linear minimum mean-squared-error (WLMMSE) estimator can achieve a lower mean-squared-error (MSE) than that of the linear minimum MSE (LMMSE) estimator.

Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]

no code implementations9 Jun 2022 Nir Shlezinger, Tirza Routtenberg

While machine learning systems often lack the interpretability of traditional signal processing methods, we focus on a simple setting where one can interpret and compare the approaches in a tractable manner that is accessible and relevant to signal processing readers.

BIG-bench Machine Learning

DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

2 code implementations22 Sep 2021 Julian P. Merkofer, Guy Revach, Nir Shlezinger, Tirza Routtenberg, Ruud J. G. van Sloun

A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.

Super-Resolution

State Estimation in Unobservable Power Systems via Graph Signal Processing Tools

no code implementations4 Jun 2021 Lital Dabush, Ariel Kroizer, Tirza Routtenberg

For simplicity, we start with analyzing the DC power flow (DC-PF) model and then extend our algorithms to the AC power flow (AC-PF) model.

Bayesian Estimation of Graph Signals

no code implementations29 Mar 2021 Ariel Kroizer, Tirza Routtenberg, Yonina C. Eldar

We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.

Identification of Edge Disconnections in Networks Based on Graph Filter Outputs

no code implementations12 Feb 2021 Shlomit Shaked, Tirza Routtenberg

We assume that the graph signals measured over the vertices of the network can be represented as white noise that has been filtered on the graph topology by a smooth graph filter.

Non-Bayesian Parametric Missing-Mass Estimation

no code implementations12 Jan 2021 Shir Cohen, Tirza Routtenberg, Lang Tong

Finally, we demonstrate via numerical simulations that the proposed mmCCRB is a valid and informative lower bound on the mmMSE of state-of-the-art estimators for this problem: the CML, the Good-Turing, and Laplace estimators.

Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data

no code implementations17 Sep 2020 Itai E. Berman, Tirza Routtenberg

We then solve the resource allocation optimization problem of the LGO model with the proposed tractable form of the MSE as an objective function and under a power constraint using a one-dimensional search.

Quantization

Low-Complexity Detection of Small Frequency Changes by the Generalized LMPU Test

no code implementations21 Aug 2020 Eyal Levy, Tirza Routtenberg

In this paper, we consider the detection of a small change in the frequency of sinusoidal signals, which arises in various signal processing applications.

Non-Bayesian Estimation Framework for Signal Recovery on Graphs

no code implementations5 May 2020 Tirza Routtenberg

We develop sampling allocation policies that optimize sensor locations in a network for these problems based on the proposed graph CRB.

Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems

no code implementations19 Mar 2020 Gal Morgenstern, Tirza Routtenberg

In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks.

Model Selection

Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation

no code implementations15 Apr 2019 Elad Meir, Tirza Routtenberg

Finally, we demonstrate in simulations that the proposed selective CRB is an informative lower bound on the performance of the maximum selected likelihood estimator for a general linear model with the generalized information criterion and for sparse vector estimation with one step thresholding.

Model Selection

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